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Evolving availability and standardization of patient attributes for matching.

Yu Deng1, Lacey P Gleason1, Adam Culbertson1

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Summary

Patient matching relies on consistent data. From 2010-2020, while core patient attributes like date of birth (DOB) remained available, newer data like gender identity and email address increased significantly, impacting patient matching performance.

Keywords:
data collectiondata completenessdata standardizationdemographic attributeselectronic health records (EHRs)interoperabilitypatient matchingrecord linkage

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Area of Science:

  • Health Informatics
  • Data Standardization
  • Patient Matching

Background:

  • Patient matching is crucial for healthcare coordination and data integrity.
  • Variations in patient attribute data across organizations hinder accurate patient matching.
  • Standardization of patient data is essential for improving healthcare system interoperability.

Purpose of the Study:

  • To analyze trends in patient attribute availability and standardization from 2010-2020.
  • To assess the impact of these changes on patient-matching performance.
  • To guide future efforts in selecting and standardizing patient attributes for improved matching.

Main Methods:

  • Surveyed 38 healthcare provider organizations on patient attribute data collection practices (2010-2020).
  • Conducted electronic health record queries for a subset of 20 sites.
  • Analyzed the availability and usage of various patient attributes over a decade.

Main Results:

  • Core attributes like name and date of birth (DOB) remained highly available (>90%).
  • Social Security Number (SSN) availability showed a slight decline in later years.
  • Attributes such as gender identity, language, and email address saw significant increases in availability (>50%).

Conclusions:

  • Patient attribute availability has evolved significantly between 2010-2020.
  • Increasing availability of diverse patient attributes presents opportunities for enhanced patient matching.
  • Understanding these variations is key to developing effective data standardization strategies for better patient matching in the US.